On-Demand and Model-Driven Case Building Based on Distributed Data Sources

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Abstract

The successful application of Case-Based Reasoning (CBR) depends on the availability of data. In most manufacturing companies these data are present, but distributed over many different systems. The distribution of the data makes it difficult to apply CBR in real-time, as data have to be collected from the different systems. In this work we propose a framework and algorithm to efficiently build a case representation on-demand and solve the challenge of distributed data in CBR. The main contribution of this work is a framework using an index for objects and the sources where data about those objects can be found. Next to the framework, we present an algorithm that operates on the framework and can be used to build case representations and construct a case base on-demand, using data from distributed sources. There are several parameters that influence the performance of the framework. Accordingly, we show in a conceptual and experimental evaluation that in highly-distributed and segregated environments the proposed approach reduces the time complexity from polynomial to linear order.
Original languageEnglish
Title of host publicationCase-Based Reasoning Research and Development
Subtitle of host publication31st International Conference, ICCBR 2023, Aberdeen, UK, July 17–20, 2023, Proceedings
EditorsStewart Massie, Sutanu Chakraborti
Place of PublicationCham
PublisherSpringer
Chapter5
Pages69-84
Number of pages16
ISBN (Electronic)978-3-031-40177-0
ISBN (Print)978-3-031-40176-3
DOIs
Publication statusPublished - 30 Jul 2023
Event31st International Conference on Case-Based Reasoning Research and Development, ICCBR 2023 - Aberdeen, United Kingdom
Duration: 17 Jul 202320 Jul 2023

Publication series

NameLecture Notes in Computer Science (LNCS)
Volume14141
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence (LNAI)
Volume14141

Conference

Conference31st International Conference on Case-Based Reasoning Research and Development, ICCBR 2023
Abbreviated titleICCBR 2023
Country/TerritoryUnited Kingdom
CityAberdeen
Period17/07/2320/07/23

Funding

Acknowledgments. We would like to thank the following people for contributing to the compilation of the CBR framework fact sheets: Ikechukwu Nkisi-Orji and Chamath Palihawadana (CloodCBR), Beatriz López, Oscar Raya, and Jonah Fernán-dez (eXiT*CBR), Juan Antonio Recio García (jColibri), and Pascal Reuss (myCBR). This work is funded by the Federal Ministry for Economic Affairs and Climate Action under grant No. 01MD22002C EASY. C21, funded by the Ministry of Science and Innovation of Spain (MCIN/AEI/ 10.13039/501100011033) and the BOSCH-UCM Honorary Chair on Artificial Intelligence applied to Internet of Things. Acknowledgments. This work was funded in part by the Department of the Navy, Office of Naval Research (Award N00014-19-1-2655). Acknowledgements. This research is a result of the Horizon 2020 Future and Emerging Technologies (FET) programme of the European Union through the iSee project (CHIST-ERA-19-XAI-008, PCI2020-120720-2). This research is funded by the iSee project. iSee is an EU CHIST-ERA project which received funding for the UK from EPSRC under grant number EP/V061755/1, for Ireland from the Irish Research Council under grant number CHIST-ERA-2019-iSee and for Spain from the MCIN/AEI and European Union “NextGenerationEU/PRTR” under grant number PCI2020-120720-2. Acknowledgements. This work has been funded by the DFG within the project ReCAP-II (No. 375342983) as part of the priority program RATIO (SPP-1999) as well as the Studienstiftung. Acknowledgments. This work is supported by the National Natural Science Foundation of China under Grant No. 62162046, the Inner Mongolia Science and Technology Project under Grant No. 2021GG0155, the Natural Science Foundation of Major Research Plan of Inner Mongolia under Grant No. 2019ZD15, and the Inner Mongolia Natural Science Foundation under Grant No. 2019GG372. Supported by the PERXAI project PID2020-114596RB-C21, funded by the Ministry of Science and Innovation of Spain (MCIN/AEI/10.13039/501100011033) and the BOSCH-UCM Honorary Chair on Artificial Intelligence applied to Internet of Things. Acknowledgments. This work was funded by the US Department of Defense (Contract W52P1J2093009), and by the Department of the Navy, Office of Naval Research (Award N00014-19-1-2655). Acknowledgements. This work is funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK) under grant no. 22973. Acknowledgements. This work was funded by Science Foundation Ireland through the SFI Centre for Research Training in Machine Learning (Grant No. 18/CRT/6183) with support from Microsoft Ireland. We wish to thank Sarah Jane Delany for comments on an earlier draft of this paper. Acknowledgements. This work was supported by the National Science Foundation of China (Granted No: 62172426). Acknowledgements. This project is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957204, the project MAS4AI (Multi-Agent Systems for Pervasive Artificial Intelligence for assisting Humans in Modular Production). In special we would like to thank the project partners for providing insights in their use cases and the reviewers for providing valuable comments and suggestions.

FundersFunder number
Horizon 2020 Future and Emerging Technologies
Microsoft Ireland
Natural Science Foundation of Major Research Plan of Inner Mongolia2019ZD15
U.S. Department of DefenseW52P1J2093009
Office of Naval ResearchN00014-19-1-2655
U.S. Navy
European Union 's Horizon 2020 - Research and Innovation Framework Programme957204
H2020 Future and Emerging Technologies
Bundesministerium für Wirtschaft und Klimaschutz01MD22002C EASY, 22973
Engineering and Physical Sciences Research CouncilEP/V061755/1
European CommissionPCI2020-120720-2, CHIST-ERA-19-XAI-008
Science Foundation Ireland - SFI18/CRT/6183
Deutsche ForschungsgemeinschaftSPP-1999, 375342983
National Natural Science Foundation of China62162046, 62172426
Irish Research CouncilCHIST-ERA-2019-iSee
Studienstiftung des Deutschen Volkes
Natural Science Foundation of Inner Mongolia Autonomous Region2019GG372
Ministerio de Ciencia e InnovaciónMCIN/AEI/10.13039/501100011033
Agencia Estatal de Investigación
Science and Technology Major Project of Inner Mongolia2021GG0155

    Keywords

    • CBR frameworks
    • Case representation
    • Case base building
    • Distributed systems
    • Industry 4.0
    • Semantic Web

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